Markov Random Fields on an Infinite Tree

An approach to reducing the labeling cost of Markov random fields within an infinite label space

This work proposes a novel idea, called SOIL, for reducing the computational complexity of the maximum a posteriori optimization problem using Markov random "elds (MRFs). The local characteristics of MRFs are employed so that the searching in a virtually in"nite label space is con"ned in a small "nite space. Globally, the number of labels allowed is as many as the number of image sites while lo...

Markov Random Fields and Conditional Random Fields

Markov chains provided us with a way to model 1D objects such as contours probabilistically, in a way that led to nice, tractable computations. We now consider 2D Markov models. These are more powerful, but not as easy to compute with. In addition we will consider two additional issues. First, we will consider adding observations to our models. These observations are conditioned on the value of...

Image Segmentation Using Binary Tree Structured Markov Random Fields

Markov Random Topic Fields

Most approaches to topic modeling assume an independence between documents that is frequently violated. We present an topic model that makes use of one or more user-specified graphs describing relationships between documents. These graph are encoded in the form of a Markov random field over topics and serve to encourage related documents to have similar topic structures. Experiments on show upw...

Combinatorial Markov Random Fields

A combinatorial random variable is a discrete random variable defined over a combinatorial set (e.g., a power set of a given set). In this paper we introduce combinatorial Markov random fields (Comrafs), which are Markov random fields where some of the nodes are combinatorial random variables. We argue that Comrafs are powerful models for unsupervised and semi-supervised learning. We put Comraf...